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Geospatial Data Science Jobs (NOW HIRING)

Planet Fitness

Sr. Geospatial Data Scientist

Boston, MA · On-site

Reporting to the Director, Geospatial Analytics & Data Science, the Senior Geospatial Data Scientist develops advanced geospatial analytical solutions, predictive models, optimization algorithms, and ...

Unity Compass

Data Scientist - SME

Alexandria, VA · On-site

$110 - $215/hr

Serve as the technical SME in data science and geospatial analytics, providing high-level guidance on complex technical problems related to the GEOINT mission. * Lead the design and development of ...

New

Vantor

Senior Geospatial Data Engineer

Mclean, VA · On-site

$116K - $139K/yr

... Science, Data Science, Engineering, or a related technical discipline. • 12+ years of ... geospatial technology, software development, data engineering, data analysis, or a related ...

MAXAR TECHNOLOGIES, INC.

$140 - $206/hr

Apply geospatial data science techniques to identify patterns, trends, and mission-relevant insights* Create maps, visualizations, and analytic products for technical and non-technical audiences*

Maxar

Senior Geospatial Data Scientist

Mclean, VA · On-site

$140K - $187K/yr

Apply geospatial data science techniques to identify patterns, trends, and mission-relevant insights * Create maps, visualizations, and analytic products for technical and non-technical audiences

Delan Associates, Inc

Sr. Data Scientist

Juno Beach, FL · On-site

... geospatial data into actionable insights. The position requires deep expertise in data science techniques and handson experience with spatial analytics at scale Responsibilities: * This position ...

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Geospatial Data Science information

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$22K

$77.5K

$122K

How much do geospatial data science jobs pay per year?

As of Aug 22, 2026, the average yearly pay for geospatial data science in the United States is $77,494.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,500.00 and $80,000.00 per year, depending on experience, location, and employer.

What is geospatial data science?

Geospatial data science is an interdisciplinary field that focuses on analyzing and interpreting data that has a geographic or spatial component. It combines techniques from data science, statistics, and geographic information systems (GIS) to extract insights, identify patterns, and solve problems related to location-based data. Professionals in this field work with mapping, remote sensing, spatial analysis, and visualization tools to support decision-making in areas like urban planning, environmental monitoring, and logistics.

What are the key skills and qualifications needed to thrive as a geospatial data scientist?

To thrive as a Geospatial Data Scientist, you need a solid background in statistics, spatial analysis, and programming, typically supported by a degree in geography, computer science, or a related field. Proficiency with GIS software (such as ArcGIS or QGIS), spatial databases, and coding languages like Python or R is essential, and certifications in GIS can be advantageous. Strong problem-solving skills, attention to detail, and effective communication help translate complex spatial data into actionable insights for diverse stakeholders. These skills ensure accurate data analysis, innovative solutions, and impactful decision-making in fields reliant on geographic information.

How does a geospatial data scientist typically collaborate with other departments or teams within an organization?

Geospatial Data Scientists often work closely with professionals from diverse departments such as urban planning, environmental science, IT, and business analytics. Collaboration usually involves sharing spatial insights, integrating geospatial data with other datasets, and contributing to interdisciplinary projects that require spatial analysis or mapping. Effective communication is crucial, as you'll translate complex geospatial findings into actionable recommendations for non-technical stakeholders. This cross-functional teamwork not only broadens your understanding of organizational goals but also enhances the impact and visibility of geospatial analyses.

What is the difference between Geospatial Data Science vs GIS Analyst?

AspectGeospatial Data ScienceGIS Analyst
Required CredentialsDegree in Data Science, Geography, or related; often includes programming skillsDegree in Geography, GIS, or related; GIS certifications common
Work EnvironmentData analysis, modeling, programming, often in tech or research settingsMapping, spatial data management, using GIS software in various industries
Employer & Industry UsageTech companies, research institutions, government agencies focusing on spatial data analysisUrban planning, environmental agencies, utilities, and government agencies

While both roles work with spatial data, Geospatial Data Science emphasizes data analysis, modeling, and programming skills to extract insights from geospatial data. GIS Analysts focus more on mapping, data management, and using GIS software for spatial analysis. The roles often overlap but differ mainly in technical focus and application areas.

More about Geospatial Data Science jobs

What cities are hiring for Geospatial Data Science jobs?

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What are the most commonly searched types of Geospatial Data Science jobs?

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What states have the most Geospatial Data Science jobs?

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Geospatial Data Science jobs near you
Infographic showing various Geospatial Data Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $77,494 per year, or $37.3 per hour.

Senior Geospatial Data Scientist/Geospatial AI/ML Scientist

VDart, Inc.

Juno Beach, FL • On-site

Other

Posted 5 days ago

Job description

The Senior Data Scientist Geospatial is a technical leader responsible for designing, developing, and delivering advanced data science, spatial analytics, and machine learning solutions using geospatial data. This role leads complex analytics initiatives, develops scalable analytical applications and spatial data products, establishes technical standards, and mentors junior data scientists and technical team members.

The position partners closely with GIS teams, business stakeholders, and technology leaders to translate complex geospatial and business challenges into scalable, production-ready solutions that deliver measurable business impact.

Key Responsibilities
  • Lead end-to-end data science and geospatial analytics initiatives, from problem definition and data exploration through model development, deployment, and monitoring.
  • Design and develop predictive, prescriptive, and spatial analytics models using large-scale geospatial and enterprise datasets.
  • Architect and deliver scalable GIS tools, analytical applications, spatial data products, and reusable geoprocessing capabilities.
  • Establish technical standards, development frameworks, coding practices, and architectural patterns for enterprise geospatial solutions.
  • Design and oversee spatial data pipelines, APIs, automation workflows, and geoprocessing frameworks.
  • Develop production-grade analytical tools and applications using Python and modern data science technologies.
  • Integrate GIS capabilities with cloud platforms, enterprise data systems, APIs, and AI/ML services.
  • Engineer and optimize features, analytical workflows, and machine learning models for performance, scalability, and reliability.
  • Implement model validation, testing, monitoring, and governance frameworks.
  • Develop automated spatial analysis workflows and reusable geoprocessing components.
  • Collaborate with GIS professionals, data engineers, software engineers, and business leaders to deliver enterprise-level solutions.
  • Translate complex analytical and geospatial findings into clear, actionable recommendations for technical and non-technical stakeholders.
  • Provide technical leadership and mentorship to junior and mid-level data scientists.
  • Evaluate emerging GIS, AI, ML, and data science technologies and recommend solutions aligned with business objectives.
  • Apply strong business acumen and independent problem-solving skills to address complex analytical challenges.
Required Qualifications
  • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Geography, GIS, Engineering, or another quantitative discipline; advanced degree preferred.
  • 6 10 years of professional experience in data science, analytics, machine learning, or a closely related field.
  • 6 8+ years of relevant experience for senior-level geospatial data science responsibilities.
  • Strong recent experience applying data science techniques to GIS/geospatial problems, preferably within the last 4 years.
  • Strong proficiency in Python or R, with demonstrated experience developing production-ready analytical workflows.
  • Advanced SQL skills and experience working with large-scale datasets.
  • Strong knowledge of statistical inference, machine learning, experimentation design, and predictive modeling.
  • Hands-on experience developing and deploying production-grade models, analytical applications, or data products.
  • Extensive experience with spatial analytics and geospatial data at scale.
  • Strong experience developing enterprise-grade GIS tools, applications, pipelines, and automation workflows.
  • Demonstrated ability to independently lead complex technical initiatives from concept through production.

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